在 X 上看到这个分享,不得不说,Ilya 真是技术宅,发的图片,还是糊的……
用 macOS 的文字识别摘下来慢慢看。
- Communicate clearly and precisely when writing prompts. The ability to clearly state tasks and describe concepts is crucial.
- Be willing to iterate rapidly, sending many prompts to the model in quick succession.
Good prompt engineers are comfortable with constant back-and-forth refinement. - Consider edge cases and unusual scenarios when designing prompts. Think about how your prompt might fail in atypical situations.
- Test your prompts with imperfect, realistic user inputs. Don’t assume users will provide perfectly formatted or grammatically correct queries.
- Read and analyze model outputs carefully. Pay close attention to whether the model is following instructions as intended.
- Strip away assumptions and clearly communicate the full set of information needed for a task. Break down the task systematically to ensure all necessary details are included.
- Think about the “theory of mind” of the model when writing prompts. Consider how the model might interpret your instructions differently than intended.
- Use version control and track experiments when working with prompts. Treat prompts like code in terms of management and iteration.
- Ask the model to identify unclear parts or ambiguities in your instructions. This can help refine and improve your prompts.
- Be precise without overcomplicating. Aim for clear task descriptions without building unnecessary abstractions.
- Consider the balance between typical cases and edge cases. While handling edge cases is important, don’t neglect the primary use case.
- Think about how prompts integrate into larger systems. Consider factors like data sources, latency, and overall system design.
- Don’t rely solely on writing skills; prompt engineering requires a mix of clear communication and systematic thinking. Good writers aren’t necessarily good prompt engineers, and vice versa.
- When working with customers, help them understand the realities of user input.
Guide them to consider real-world usage patterns rather than idealized scenarios. - Practice looking at data and model outputs extensively. Familiarize yourself with how the model responds to different types of prompts and inputs.
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